feat(FastEmbed): removed old TODO (fixed)

deepset-ai · Mar 13, 2024 · a9b3827 · a9b3827
1 parent e1c5602
commit a9b3827
Show file tree

Hide file tree

Showing 4 changed files with 0 additions and 7 deletions.
diff --git a/...aystack_integrations/components/embedders/fastembed/fastembed_document_SPLADE_embedder.py b/...aystack_integrations/components/embedders/fastembed/fastembed_document_SPLADE_embedder.py
@@ -159,8 +159,6 @@ def run(self, documents: List[Document]):
             msg = "The embedding model has not been loaded. Please call warm_up() before running."
             raise RuntimeError(msg)
 
-        # TODO: once non textual Documents are properly supported, we should also prepare them for embedding here
-
         texts_to_embed = self._prepare_texts_to_embed(documents=documents)
         embeddings = self.embedding_backend.embed(
             texts_to_embed,

diff --git a/...d/src/haystack_integrations/components/embedders/fastembed/fastembed_document_embedder.py b/...d/src/haystack_integrations/components/embedders/fastembed/fastembed_document_embedder.py
@@ -157,8 +157,6 @@ def run(self, documents: List[Document]):
             msg = "The embedding model has not been loaded. Please call warm_up() before running."
             raise RuntimeError(msg)
 
-        # TODO: once non textual Documents are properly supported, we should also prepare them for embedding here
-
         texts_to_embed = self._prepare_texts_to_embed(documents=documents)
         embeddings = self.embedding_backend.embed(
             texts_to_embed,

diff --git a/integrations/fastembed/tests/test_fastembed_document_SPLADE_embedder.py b/integrations/fastembed/tests/test_fastembed_document_SPLADE_embedder.py
@@ -223,7 +223,6 @@ def test_embed(self):
         assert len(result["documents"]) == len(documents)
         for doc in result["documents"]:
             assert isinstance(doc, Document)
-            # TODO adapt for sparse
             assert isinstance(doc.meta["_sparse_vector"], dict)
             assert isinstance(doc.meta["_sparse_vector"]["indices"], list)
             assert isinstance(doc.meta["_sparse_vector"]["indices"][0], int)
@@ -292,7 +291,6 @@ def test_run(self):
         result = embedder.run(documents=[doc])
         # TODO adapt for sparse
         embedding = result["documents"][0].embedding
-        # TODO adapt for sparse
         assert isinstance(embedding, list)
         assert len(embedding) == 384
         assert all(isinstance(emb, float) for emb in embedding)
diff --git a/integrations/fastembed/tests/test_fastembed_text_SPLADE_embedder.py b/integrations/fastembed/tests/test_fastembed_text_SPLADE_embedder.py
@@ -201,7 +201,6 @@ def test_embed(self):
 
         result = embedder.run(text=text)
         embedding = result["embedding"]
-        # TODO adapt to sparse
         assert isinstance(embedding, dict)
         assert isinstance(embedding["indices"], list)
         assert isinstance(embedding["indices"][0], int)